开源平衡轮机器人数据集，含1kHz高精度多模态数据。
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
- 采集1kHz同步数据，包含传感器、状态估计、动作捕捉真值与第三人称视频。
- 覆盖多种硬件、表面和控制策略，提升数据多样性与泛化性。
- 适合机器人动力学建模、状态估计与时序分类算法的基准测试。
开发稳定系统的学习型控制算法需要高质量的真实世界数据，但专用机器人硬件的获取对许多研究者仍是重大障碍。本文介绍了针对迷你轮式机器人（Mini Wheelbot）的综合性动力学数据集，该机器人为开源、准对称的平衡式反应轮独轮车。数据集提供1kHz同步数据，涵盖所有机载传感器读数、状态估计、动作捕捉系统的真值位姿以及第三人称视频日志。为确保数据多样性，实验覆盖多个硬件实例、不同表面，并采用多种控制范式，包括伪随机二进制激励、非线性模型预测控制及强化学习智能体。我们还提供了若干示例应用，涵盖动力学建模、状态估计与时序分类任务，展示该数据集可支持常见机器人算法的基准测试。
原文摘要 · Abstract (English)
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics algorithms that can be benchmarked on our dataset.
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